Chapter 7 Functional differences
7.1 Data preparation
# Aggregate bundle-level GIFTs into the compound level
GIFTs_elements <- to.elements(genome_gifts, GIFT_db3)
GIFTs_elements_filtered <- GIFTs_elements[rownames(GIFTs_elements) %in% genome_counts$genome, ]
GIFTs_elements_filtered <- as.data.frame(GIFTs_elements_filtered) %>%
select_if(~ !is.numeric(.) || sum(.) != 0)
elements <- GIFTs_elements_filtered %>%
as.data.frame()
# Aggregate element-level GIFTs into the function level
GIFTs_functions <- to.functions(GIFTs_elements_filtered, GIFT_db3)
functions <- GIFTs_functions %>%
as.data.frame()
# Aggregate function-level GIFTs into overall Biosynthesis, Degradation and Structural GIFTs
GIFTs_domains <- to.domains(GIFTs_functions, GIFT_db3)
domains <- GIFTs_domains %>%
as.data.frame()
# Get community-weighed average GIFTs per sample
GIFTs_elements_community <- to.community(GIFTs_elements_filtered, genome_counts_filt %>% column_to_rownames(., "genome") %>% tss(), GIFT_db3)
GIFTs_functions_community <- to.community(GIFTs_functions, genome_counts_filt %>% column_to_rownames(., "genome") %>% tss(), GIFT_db3)
GIFTs_domains_community <- to.community(GIFTs_domains, genome_counts_filt %>% column_to_rownames(., "genome") %>% tss(), GIFT_db3)
uniqueGIFT_db<- unique(GIFT_db3[c(2,4,5,6)]) %>% unite("Function",Function:Element, sep= "_", remove=FALSE)7.2 Genomes GIFT profiles
GIFTs_elements %>%
as_tibble(., rownames = "MAG") %>%
reshape2::melt() %>%
rename(Code_element = variable, GIFT = value) %>%
inner_join(GIFT_db3,by="Code_element") %>%
ggplot(., aes(x=Code_element, y=MAG, fill=GIFT, group=Code_function))+
geom_tile()+
scale_y_discrete(guide = guide_axis(check.overlap = TRUE))+
scale_x_discrete(guide = guide_axis(check.overlap = TRUE))+
scale_fill_gradientn(colours=rev(c("#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#ddf1da")))+
facet_grid(. ~ Code_function, scales = "free", space = "free")+
theme_grey(base_size=8)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),strip.text.x = element_text(angle = 90))
7.3 Function level
GIFTs_functions_community %>%
as.data.frame() %>%
rownames_to_column(var="sample") %>%
filter(sample!="AD69") %>%
pivot_longer(!sample,names_to="trait",values_to="gift") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
ggplot(aes(x=trait,y=time_point,fill=gift)) +
geom_tile(colour="white", size=0.2)+
scale_fill_gradientn(colours=rev(c("#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#ddf1da")))+
facet_grid(type ~ ., scales="free",space="free")
7.4 Element level
GIFTs_elements_community_merged<-GIFTs_elements_community %>%
as.data.frame() %>%
rownames_to_column(var="sample") %>%
filter(sample!="AD69") %>%
pivot_longer(!sample,names_to="trait",values_to="gift") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code))%>%
mutate(functionid = substr(trait, 1, 3)) %>%
mutate(trait = case_when(
trait %in% GIFT_db3$Code_element ~ GIFT_db3$Element[match(trait, GIFT_db3$Code_element)],
TRUE ~ trait
)) %>%
mutate(functionid = case_when(
functionid %in% GIFT_db3$Code_function ~ GIFT_db3$Function[match(functionid, GIFT_db3$Code_function)],
TRUE ~ functionid
)) %>%
mutate(trait=factor(trait,levels=unique(GIFT_db3$Element))) %>%
mutate(functionid=factor(functionid,levels=unique(GIFT_db3$Function)))
# Create an interaction variable for time_point and sample
GIFTs_elements_community_merged$interaction_var <- interaction(GIFTs_elements_community_merged$sample, GIFTs_elements_community_merged$time_point)
ggplot(GIFTs_elements_community_merged,aes(x=interaction_var,y=trait,fill=gift)) +
geom_tile(colour="white", linewidth=0.2)+
scale_fill_gradientn(colours=rev(c("#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#ddf1da")))+
facet_grid(functionid ~ type, scales="free",space="free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, size=5),
strip.text.y = element_text(angle = 0)) +
labs(y="Traits",x="Time_point",fill="GIFT")+
scale_x_discrete(labels = function(x) gsub(".*\\.", "", x))
7.5 Comparison of samples from the 0 Time_point (0_Wild)
7.5.1 GIFTs Functional community
GIFTs_functions_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="0_Wild") %>%
group_by(species) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 2 × 3
species MCI sd
<chr> <dbl> <dbl>
1 Podarcis_liolepis 0.271 0.0203
2 Podarcis_muralis 0.281 0.01467.5.1.1 GIFT test visualisation
GIFTs_functions_community %>%
as.data.frame() %>%
rownames_to_column("sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="0_Wild") %>%
select(c(1:30, 33)) %>%
pivot_longer(-c(sample,Population),names_to = "trait", values_to = "value") %>%
mutate(trait = case_when(
trait %in% GIFT_db3$Code_function ~ GIFT_db3$Function[match(trait, GIFT_db3$Code_function)],
TRUE ~ trait
)) %>%
mutate(trait=factor(trait,levels=unique(GIFT_db3$Function))) %>%
ggplot(aes(x=value, y=Population, group=Population, fill=Population, color=Population)) +
geom_boxplot() +
scale_color_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#008080', "#d57d2c")) +
scale_fill_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#00808050', "#d57d2c50")) +
facet_grid(trait ~ ., space="free", scales="free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
strip.text.y = element_text(angle = 0)) +
labs(y="Traits",x="Metabolic capacity index")
7.5.2 GIFTs Domain community
GIFTs_domains_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="0_Wild") %>%
group_by(species) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 2 × 3
species MCI sd
<chr> <dbl> <dbl>
1 Podarcis_liolepis 0.283 0.0162
2 Podarcis_muralis 0.289 0.01847.5.3 GIFTs Elements community
GIFTs_elements_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="0_Wild") %>%
group_by(species) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 2 × 3
species MCI sd
<chr> <dbl> <dbl>
1 Podarcis_liolepis 0.241 0.0239
2 Podarcis_muralis 0.260 0.0163sample_metadata_wild <- sample_metadata%>%
filter(time_point == "0_Wild")
element_gift_wild <- GIFTs_elements_community %>%
as.data.frame() %>%
rownames_to_column(., "Tube_code") %>%
inner_join(., sample_metadata_wild[c(1,3)], by="Tube_code")# Find numeric columns
numeric_cols <- sapply(element_gift_wild, is.numeric)
# Calculate column sums for numeric columns only
col_sums_numeric <- colSums(element_gift_wild[, numeric_cols])
# Identify numeric columns with sums not equal to zero
nonzero_numeric_cols <- names(col_sums_numeric)[col_sums_numeric != 0]
# Remove numeric columns with sums not equal to zero
filtered_data <- element_gift_wild[, !numeric_cols | colnames(element_gift_wild) %in% nonzero_numeric_cols]significant_elements_wild <- filtered_data %>%
pivot_longer(-c(Tube_code,species), names_to = "trait", values_to = "value") %>%
group_by(trait) %>%
summarise(p_value = wilcox.test(value ~ species, exact=FALSE)$p.value) %>%
mutate(p_adjust=p.adjust(p_value, method="BH")) %>%
filter(p_adjust < 0.05) %>%
left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(trait == Code_element))
element_gift_t <- element_gift_wild %>%
dplyr::select(-c(species)) %>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "trait")
element_gift_filt <- subset(element_gift_t, trait %in% significant_elements_wild$trait) %>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Tube_code")%>%
left_join(., sample_metadata_wild[c(1,3)], by = join_by(Tube_code == Tube_code))
element_gift_filt %>%
dplyr::select(-Tube_code)%>%
group_by(species) %>%
summarise(across(everything(), mean))%>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Elements") %>%
left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))
element_gift_names <- element_gift_filt%>%
dplyr::select(-species)%>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Elements") %>%
left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))%>%
dplyr::select(-Elements)%>%
dplyr::select(Function, everything())%>%
t()%>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Tube_code")%>%
left_join(., sample_metadata_wild[c(1,4)], by = join_by(Tube_code == Tube_code))colNames <- names(element_gift_names)[2:72] #always check names(element_gift_names) first to know where your traits finish
for(i in colNames){
plt <- ggplot(element_gift_names, aes(x=Population, y=.data[[i]], color = Population)) +
geom_boxplot(alpha = 0.2, outlier.shape = NA, width = 0.3, show.legend = FALSE) +
geom_jitter(width = 0.1, show.legend = TRUE) +
theme_minimal() +
theme(
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank())
print(plt)
}
7.6 Comparison of samples from the 1st Time_point (1_Acclimation)
7.6.1 GIFTs Functional community
GIFTs_functions_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="1_Acclimation") %>%
group_by(species) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 2 × 3
species MCI sd
<chr> <dbl> <dbl>
1 Podarcis_liolepis 0.284 0.0148
2 Podarcis_muralis 0.273 0.02587.6.1.1 GIFT test visualisation
GIFTs_functions_community %>%
as.data.frame() %>%
rownames_to_column("sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="1_Acclimation") %>%
select(c(1:30, 33)) %>%
pivot_longer(-c(sample,Population),names_to = "trait", values_to = "value") %>%
mutate(trait = case_when(
trait %in% GIFT_db3$Code_function ~ GIFT_db3$Function[match(trait, GIFT_db3$Code_function)],
TRUE ~ trait
)) %>%
mutate(trait=factor(trait,levels=unique(GIFT_db3$Function))) %>%
ggplot(aes(x=value, y=Population, group=Population, fill=Population, color=Population)) +
geom_boxplot() +
scale_color_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#008080', "#d57d2c")) +
scale_fill_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#00808050', "#d57d2c50")) +
facet_grid(trait ~ ., space="free", scales="free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
strip.text.y = element_text(angle = 0)) +
labs(y="Traits",x="Metabolic capacity index")
7.6.2 GIFTs Domain community
GIFTs_domains_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="1_Acclimation") %>%
group_by(species) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 2 × 3
species MCI sd
<chr> <dbl> <dbl>
1 Podarcis_liolepis 0.286 0.0130
2 Podarcis_muralis 0.277 0.03067.6.3 GIFTs Elements community
GIFTs_elements_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="1_Acclimation") %>%
group_by(species) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 2 × 3
species MCI sd
<chr> <dbl> <dbl>
1 Podarcis_liolepis 0.264 0.0182
2 Podarcis_muralis 0.254 0.0271sample_metadata_accli <- sample_metadata%>%
filter(time_point == "1_Acclimation")
element_gift_accli <- GIFTs_elements_community %>%
as.data.frame() %>%
rownames_to_column(., "Tube_code") %>%
inner_join(., sample_metadata_accli[c(1,3)], by="Tube_code")# Find numeric columns
numeric_cols <- sapply(element_gift_accli, is.numeric)
# Calculate column sums for numeric columns only
col_sums_numeric <- colSums(element_gift_accli[, numeric_cols])
# Identify numeric columns with sums not equal to zero
nonzero_numeric_cols <- names(col_sums_numeric)[col_sums_numeric != 0]
# Remove numeric columns with sums not equal to zero
filtered_data <- element_gift_accli[, !numeric_cols | colnames(element_gift_accli) %in% nonzero_numeric_cols]significant_elements_accli <- filtered_data %>%
pivot_longer(-c(Tube_code,species), names_to = "trait", values_to = "value") %>%
group_by(trait) %>%
summarise(p_value = wilcox.test(value ~ species, exact=FALSE)$p.value) %>%
mutate(p_adjust=p.adjust(p_value, method="BH")) %>%
filter(p_adjust < 0.05) %>%
left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(trait == Code_element))
element_gift_t <- element_gift_accli %>%
dplyr::select(-c(species)) %>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "trait")
element_gift_filt <- subset(element_gift_t, trait %in% significant_elements_accli$trait) %>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Tube_code")%>%
left_join(., sample_metadata_accli[c(1,3)], by = join_by(Tube_code == Tube_code))
element_gift_filt %>%
dplyr::select(-Tube_code)%>%
group_by(species) %>%
summarise(across(everything(), mean))%>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Elements") %>%
left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))
element_gift_names <- element_gift_filt%>%
dplyr::select(-species)%>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Elements") %>%
left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))%>%
dplyr::select(-Elements)%>%
dplyr::select(Function, everything())%>%
t()%>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Tube_code")%>%
left_join(., sample_metadata_accli[c(1,4)], by = join_by(Tube_code == Tube_code))colNames <- names(element_gift_names)[2:51] #always check names(element_gift_names) first to know where your traits finish
for(i in colNames){
plt <- ggplot(element_gift_names, aes(x=Population, y=.data[[i]], color = Population)) +
geom_boxplot(alpha = 0.2, outlier.shape = NA, width = 0.3, show.legend = FALSE) +
geom_jitter(width = 0.1, show.legend = TRUE) +
theme_minimal() +
theme(
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank())
print(plt)
}
7.7 Comparison of samples from the 5th Time_point (5_Post-FMT1)
7.7.1 GIFTs Functional community
GIFTs_functions_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="5_Post-FMT1") %>%
group_by(type) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 3 × 3
type MCI sd
<chr> <dbl> <dbl>
1 Control 0.308 0.0247
2 Hot_control 0.309 0.0464
3 Treatment 0.291 0.01207.7.1.1 GIFT test visualisation
GIFTs_functions_community %>%
as.data.frame() %>%
rownames_to_column("sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="5_Post-FMT1") %>%
select(c(1:30, 36)) %>%
pivot_longer(-c(sample,type),names_to = "trait", values_to = "value") %>%
mutate(trait = case_when(
trait %in% GIFT_db3$Code_function ~ GIFT_db3$Function[match(trait, GIFT_db3$Code_function)],
TRUE ~ trait
)) %>%
mutate(trait=factor(trait,levels=unique(GIFT_db3$Function))) %>%
ggplot(aes(x=value, y=type, group=type, fill=type, color=type)) +
geom_boxplot() +
scale_color_manual(name="type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_fill_manual(name="type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_grid(trait ~ ., space="free", scales="free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
strip.text.y = element_text(angle = 0)) +
labs(y="Traits",x="Metabolic capacity index")
7.7.2 GIFTs Domain community
GIFTs_domains_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="5_Post-FMT1") %>%
group_by(type) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 3 × 3
type MCI sd
<chr> <dbl> <dbl>
1 Control 0.305 0.0230
2 Hot_control 0.307 0.0447
3 Treatment 0.287 0.01417.7.3 GIFTs Elements community
GIFTs_elements_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="5_Post-FMT1") %>%
group_by(type) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 3 × 3
type MCI sd
<chr> <dbl> <dbl>
1 Control 0.293 0.0303
2 Hot_control 0.293 0.0467
3 Treatment 0.275 0.0137sample_metadata_tm5 <- sample_metadata%>%
filter(time_point == "5_Post-FMT1")%>%
filter(type != "Hot_control")
element_gift_tm5 <- GIFTs_elements_community %>%
as.data.frame() %>%
rownames_to_column(., "Tube_code") %>%
inner_join(sample_metadata_tm5 %>% select(1, 7), by = "Tube_code")# Find numeric columns
numeric_cols <- sapply(element_gift_tm5, is.numeric)
# Calculate column sums for numeric columns only
col_sums_numeric <- colSums(element_gift_tm5[, numeric_cols])
# Identify numeric columns with sums not equal to zero
nonzero_numeric_cols <- names(col_sums_numeric)[col_sums_numeric != 0]
# Remove numeric columns with sums not equal to zero
filtered_data <- element_gift_tm5[, !numeric_cols | colnames(element_gift_tm5) %in% nonzero_numeric_cols]significant_elements_tm5 <- filtered_data %>%
pivot_longer(-c(Tube_code,type), names_to = "trait", values_to = "value") %>%
group_by(trait) %>%
summarise(p_value = wilcox.test(value ~ type, exact=FALSE)$p.value) %>%
mutate(p_adjust=p.adjust(p_value, method="BH")) %>%
filter(p_value < 0.05) %>% #take into account that p_value is used and not p_adjust
left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(trait == Code_element))
element_gift_t <- element_gift_tm5 %>%
dplyr::select(-c(type)) %>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "trait")
element_gift_filt <- subset(element_gift_t, trait %in% significant_elements_tm5$trait) %>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Tube_code")%>%
left_join(., sample_metadata_tm5[c(1,7)], by = join_by(Tube_code == Tube_code))
element_gift_filt %>%
dplyr::select(-Tube_code)%>%
group_by(type) %>%
summarise(across(everything(), mean))%>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Elements") %>%
left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))
element_gift_names <- element_gift_filt%>%
dplyr::select(-type)%>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Elements") %>%
left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))%>%
dplyr::select(-Elements)%>%
dplyr::select(Function, everything())%>%
t()%>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Tube_code")%>%
left_join(., sample_metadata_tm5[c(1,7)], by = join_by(Tube_code == Tube_code))colNames <- names(element_gift_names)[2:14] #always check names(element_gift_names) first to now where your traits finish
for(i in colNames){
plt <- ggplot(element_gift_names, aes(x=type, y=.data[[i]], color = type)) +
geom_boxplot(alpha = 0.2, outlier.shape = NA, width = 0.3, show.legend = FALSE) +
geom_jitter(width = 0.1, show.legend = TRUE) +
theme_minimal() +
theme(
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank())
print(plt)
}
7.8 Comparison of samples from the 6th Time_point (6_Post-FMT2)
7.8.1 GIFTs Functional community
GIFTs_functions_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2") %>%
group_by(type) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 3 × 3
type MCI sd
<chr> <dbl> <dbl>
1 Control 0.287 0.0157
2 Hot_control 0.285 0.0294
3 Treatment 0.280 0.0197MCI_tm6 <- GIFTs_functions_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2")
shapiro.test(MCI_tm6$value)#normality test
Shapiro-Wilk normality test
data: MCI_tm6$value
W = 0.95761, p-value = 0.3256 Df Sum Sq Mean Sq F value Pr(>F)
type 2 0.000195 0.0000974 0.195 0.824
Residuals 24 0.011994 0.0004998 7.8.1.1 GIFT test visualisation
GIFTs_functions_community %>%
as.data.frame() %>%
rownames_to_column("sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2") %>%
select(c(1:30, 36)) %>%
pivot_longer(-c(sample,type),names_to = "trait", values_to = "value") %>%
mutate(trait = case_when(
trait %in% GIFT_db3$Code_function ~ GIFT_db3$Function[match(trait, GIFT_db3$Code_function)],
TRUE ~ trait
)) %>%
mutate(trait=factor(trait,levels=unique(GIFT_db3$Function))) %>%
ggplot(aes(x=value, y=type, group=type, fill=type, color=type)) +
geom_boxplot() +
scale_color_manual(name="type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_fill_manual(name="type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_grid(trait ~ ., space="free", scales="free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
strip.text.y = element_text(angle = 0)) +
labs(y="Traits",x="Metabolic capacity index")
7.8.2 GIFTs Domain community
GIFTs_domains_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2") %>%
group_by(type) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 3 × 3
type MCI sd
<chr> <dbl> <dbl>
1 Control 0.290 0.0210
2 Hot_control 0.292 0.0310
3 Treatment 0.282 0.0190MCI_domains_tm6 <- GIFTs_domains_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2")
shapiro.test(MCI_domains_tm6$value)#normality test
Shapiro-Wilk normality test
data: MCI_domains_tm6$value
W = 0.9498, p-value = 0.2119 Df Sum Sq Mean Sq F value Pr(>F)
type 2 0.00057 0.0002851 0.485 0.622
Residuals 24 0.01411 0.0005878 7.8.3 GIFTs Elements community
GIFTs_elements_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2") %>%
group_by(type) %>%
summarise(MCI = mean(value), sd = sd(value))# A tibble: 3 × 3
type MCI sd
<chr> <dbl> <dbl>
1 Control 0.268 0.0169
2 Hot_control 0.263 0.0314
3 Treatment 0.261 0.0219MCI_ele_tm6 <- GIFTs_elements_community %>%
rowMeans() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2")
shapiro.test(MCI_ele_tm6$value) #normality test
Shapiro-Wilk normality test
data: MCI_ele_tm6$value
W = 0.9806, p-value = 0.8757 Df Sum Sq Mean Sq F value Pr(>F)
type 2 0.000291 0.0001453 0.249 0.782
Residuals 24 0.014001 0.0005834 7.8.3.1 CI vs CC
sample_metadata_TM6 <- sample_metadata%>%
filter(time_point == "6_Post-FMT2")%>%
filter(type != "Hot_control")
element_gift_TM6 <- GIFTs_elements_community %>%
as.data.frame() %>%
rownames_to_column(., "Tube_code") %>%
inner_join(sample_metadata_TM6 %>% select(1, 7), by = "Tube_code")# Find numeric columns
numeric_cols <- sapply(element_gift_TM6, is.numeric)
# Calculate column sums for numeric columns only
col_sums_numeric <- colSums(element_gift_TM6[, numeric_cols])
# Identify numeric columns with sums not equal to zero
nonzero_numeric_cols <- names(col_sums_numeric)[col_sums_numeric != 0]
# Remove numeric columns with sums not equal to zero
filtered_data <- element_gift_TM6[, !numeric_cols | colnames(element_gift_TM6) %in% nonzero_numeric_cols]significant_elements_TM6 <- filtered_data %>%
pivot_longer(-c(Tube_code,type), names_to = "trait", values_to = "value") %>%
group_by(trait) %>%
summarise(p_value = wilcox.test(value ~ type, exact=FALSE)$p.value) %>%
mutate(p_adjust=p.adjust(p_value, method="BH")) %>%
filter(p_value < 0.05) %>% #take into account that p_value is used and not p_adjust
left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(trait == Code_element))
element_gift_t <- element_gift_TM6 %>%
dplyr::select(-c(type)) %>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "trait")
element_gift_filt <- subset(element_gift_t, trait %in% significant_elements_TM6$trait) %>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Tube_code")%>%
left_join(., sample_metadata_TM6[c(1,7)], by = join_by(Tube_code == Tube_code))
element_gift_filt %>%
dplyr::select(-Tube_code)%>%
group_by(type) %>%
summarise(across(everything(), mean))%>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Elements") %>%
left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))
element_gift_names <- element_gift_filt%>%
dplyr::select(-type)%>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Elements") %>%
left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))%>%
dplyr::select(-Elements)%>%
dplyr::select(Function, everything())%>%
t()%>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Tube_code")%>%
left_join(., sample_metadata_TM6[c(1,7)], by = join_by(Tube_code == Tube_code))colNames <- names(element_gift_names)[2:20] #always check names(element_gift_names) first to now where your traits finish
for(i in colNames){
plt <- ggplot(element_gift_names, aes(x=type, y=.data[[i]], color = type)) +
geom_boxplot(alpha = 0.2, outlier.shape = NA, width = 0.3, show.legend = FALSE) +
geom_jitter(width = 0.1, show.legend = TRUE) +
theme_minimal() +
theme(
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank())
print(plt)
}
7.8.3.2 CI vs WC
sample_metadata_TM8 <- sample_metadata%>%
filter(time_point == "6_Post-FMT2")%>%
filter(type != "Control")
element_gift_TM8 <- GIFTs_elements_community %>%
as.data.frame() %>%
rownames_to_column(., "Tube_code") %>%
inner_join(sample_metadata_TM8 %>% select(1, 7), by = "Tube_code")# Find numeric columns
numeric_cols <- sapply(element_gift_TM8, is.numeric)
# Calculate column sums for numeric columns only
col_sums_numeric <- colSums(element_gift_TM8[, numeric_cols])
# Identify numeric columns with sums not equal to zero
nonzero_numeric_cols <- names(col_sums_numeric)[col_sums_numeric != 0]
# Remove numeric columns with sums not equal to zero
filtered_data <- element_gift_TM8[, !numeric_cols | colnames(element_gift_TM8) %in% nonzero_numeric_cols]significant_elements_TM8 <- filtered_data %>%
pivot_longer(-c(Tube_code,type), names_to = "trait", values_to = "value") %>%
group_by(trait) %>%
summarise(p_value = wilcox.test(value ~ type, exact=FALSE)$p.value) %>%
mutate(p_adjust=p.adjust(p_value, method="BH")) %>%
filter(p_value < 0.05) %>% #take into account that p_value is used and not p_adjust
left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(trait == Code_element))
element_gift_t <- element_gift_TM8 %>%
dplyr::select(-c(type)) %>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "trait")
element_gift_filt <- subset(element_gift_t, trait %in% significant_elements_TM8$trait) %>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Tube_code")%>%
left_join(., sample_metadata_TM8[c(1,7)], by = join_by(Tube_code == Tube_code))
element_gift_filt %>%
dplyr::select(-Tube_code)%>%
group_by(type) %>%
summarise(across(everything(), mean))%>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Elements") %>%
left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))
element_gift_names <- element_gift_filt%>%
dplyr::select(-type)%>%
t() %>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Elements") %>%
left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))%>%
dplyr::select(-Elements)%>%
dplyr::select(Function, everything())%>%
t()%>%
row_to_names(row_number = 1) %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
rownames_to_column(., "Tube_code")%>%
left_join(., sample_metadata_TM8[c(1,7)], by = join_by(Tube_code == Tube_code))colNames <- names(element_gift_names)[2:13] #always check names(element_gift_names) first to now where your traits finish
for(i in colNames){
plt <- ggplot(element_gift_names, aes(x=type, y=.data[[i]], color = type)) +
geom_boxplot(alpha = 0.2, outlier.shape = NA, width = 0.3, show.legend = FALSE) +
geom_jitter(width = 0.1, show.legend = TRUE) +
theme_minimal() +
theme(
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank())
print(plt)
}
7.9 Domain level
7.9.1 Comparison of samples from the 0 Time_point (0_Wild)
#Merge the functional domains with the metadata
merge_gift_wild<- GIFTs_domains_community %>%
as.data.frame() %>%
rownames_to_column(., "Tube_code") %>%
inner_join(., sample_metadata_wild, by="Tube_code")#Biosynthesis
p1 <-merge_gift_wild %>%
ggplot(aes(x=species,y=Biosynthesis,color=species,fill=species))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")
#Degradation
p2 <-merge_gift_wild %>%
ggplot(aes(x=species,y=Degradation,color=species,fill=species))+
geom_jitter(width = 0.2, size = 1.45, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")
#Structure
p3 <-merge_gift_wild %>%
ggplot(aes(x=species,y=Structure,color=species,fill=species))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 3, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")
#Transport
p4 <-merge_gift_wild %>%
ggplot(aes(x=species,y=Transport,color=species,fill=species))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 3, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")
7.9.2 Comparison of samples from the 1st Time_point (1_Acclimation)
#Merge the functional domains with the metadata
merge_gift_accli<- GIFTs_domains_community %>%
as.data.frame() %>%
rownames_to_column(., "Tube_code") %>%
inner_join(., sample_metadata_accli, by="Tube_code")#Biosynthesis
p1 <-merge_gift_accli %>%
ggplot(aes(x=species,y=Biosynthesis,color=species,fill=species))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")
#Degradation
p2 <-merge_gift_accli %>%
ggplot(aes(x=species,y=Degradation,color=species,fill=species))+
geom_jitter(width = 0.2, size = 1.45, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")
#Structure
p3 <-merge_gift_accli %>%
ggplot(aes(x=species,y=Structure,color=species,fill=species))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 3, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")
#Transport
p4 <-merge_gift_accli %>%
ggplot(aes(x=species,y=Transport,color=species,fill=species))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 3, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")
7.9.3 Comparison of samples from the 5th Time_point (5_Post-FMT1)
#Merge the functional domains with the metadata
merge_gift_tm5<- GIFTs_domains_community %>%
as.data.frame() %>%
rownames_to_column(., "Tube_code") %>%
inner_join(., sample_metadata_tm5, by="Tube_code")#Biosynthesis
p1 <-merge_gift_tm5 %>%
ggplot(aes(x=type,y=Biosynthesis,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")
#Degradation
p2 <-merge_gift_tm5 %>%
ggplot(aes(x=type,y=Degradation,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.45, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")
#Structure
p3 <-merge_gift_tm5 %>%
ggplot(aes(x=type,y=Structure,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 3, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")
#Transport
p4 <-merge_gift_tm5 %>%
ggplot(aes(x=species,y=Transport,color=species,fill=species))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 3, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")Warning: Computation failed in `stat_compare_means()`.
Caused by error:
! argument "x" is missing, with no default
7.9.4 Comparison of samples from the 6th Time_point (6_Post-FMT2)
7.9.4.1 CI vs CC
#Merge the functional domains with the metadata
merge_gift_TM6 <- GIFTs_domains_community %>%
as.data.frame() %>%
rownames_to_column(., "Tube_code") %>%
inner_join(., sample_metadata_TM6, by="Tube_code")#Biosynthesis
p1 <-merge_gift_TM6 %>%
ggplot(aes(x=type,y=Biosynthesis,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Type")
#Degradation
p2 <-merge_gift_TM6 %>%
ggplot(aes(x=type,y=Degradation,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "type")
#Structure
p3 <-merge_gift_TM6 %>%
ggplot(aes(x=type,y=Structure,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 3, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "type")
#Transport
p4 <-merge_gift_TM6 %>%
ggplot(aes(x=type,y=Transport,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 3, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")
7.9.4.2 CI vs WC
#Merge the functional domains with the metadata
merge_gift_TM8 <- GIFTs_domains_community %>%
as.data.frame() %>%
rownames_to_column(., "Tube_code") %>%
inner_join(., sample_metadata_TM8, by="Tube_code")#Biosynthesis
p1 <-merge_gift_TM8 %>%
ggplot(aes(x=type,y=Biosynthesis,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Type")
#Degradation
p2 <-merge_gift_TM8 %>%
ggplot(aes(x=type,y=Degradation,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 0.5, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "type")
#Structure
p3 <-merge_gift_TM8 %>%
ggplot(aes(x=type,y=Structure,color=type,fill=type))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 3, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "type")
#Transport
p4 <-merge_gift_TM8 %>%
ggplot(aes(x=species,y=Transport,color=species,fill=species))+
geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+
geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
stat_compare_means() +
theme(axis.text.x = element_text(vjust = 3, size=10),
axis.text.y = element_text(size=10),
axis.title=element_text(size=12,face="bold"),
axis.text = element_text(face="bold", size=18),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size=10),
legend.title = element_text(size=12),
legend.position="none",
legend.key.size = unit(1, 'cm'),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
labs( x = "Species")